Abstract for yow_bmvc95

This paper describes a method to detect and locate human faces in an
image given no prior information about the size, orientation, and
viewpoint of the faces in the image. This method uses a family of
Gaussian derivative filters to search and extract human facial
features from the image and then group them together into a set of
partial faces using their geometric relationship. A belief network is
then constructed for each possible face candidate and the belief
values updated by evidences propagating through the network.
Different instances of detected faces are then compared using their
belief values and improbable face candidates discarded. The algorithm
is tested on different instances of faces with varying sizes,
orientation and viewpoint and the results indicate a 91% success rate
in detection under viewpoint variation.

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